Rhapsody: Predicting the pathogenicity of human missense variants

MOTIVATION: The biological effects of human missense variants have been studied experimentally for decades but predicting their effects in clinical molecular diagnostics remains challenging. Available computational tools are usually based on the analysis of sequence conservation and structural properties of the mutant protein. We recently introduced a new machine learning method that demonstrated for the first time the significance of protein dynamics in determining the pathogenicity of missense variants. RESULTS: Here we present a new interface (Rhapsody) that enables fully automated assessment of pathogenicity, incorporating both sequence coevolution data and structure- and dynamics-based features. Benchmarked against a dataset of about 20,000 annotated variants, the methodology is shown to outperform well-established and/or advanced prediction tools. We illustrate the utility of Rhapsody by in silico saturation mutagenesis studies of human H-Ras, PTEN and TPMT. AVAILABILITY AND IMPLEMENTATION: [R4.3] The new tool is available both as an online webserver at http://rhapsody.csb.pitt.edu and as an open source Python package (GitHub repository: https://github.com/prody/rhapsody; PyPI package installation: pip install prody-rhapsody). Links to additional resources and package documentation are provided in the "Download" and "Docs" sections of the website, respectively. SUPPLEMENTARY INFORMATION: A Supplementary Information file with additional Figures, Tables and expanded discussions on Materials and Methods is available at Bioinformatics online. All data, Python scripts and instructions needed to replicate the results presented here can be accessed through the Rhapsody website "Tutorials" page.